Python: Advanced Guide to Artificial Intelligence: Expert machine learning systems and intelligent agents using Python

by Giuseppe Bonaccorso, Armando Fandango, Rajalingappaa Shanmugamani

Demystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problems

Key FeaturesMaster supervised, unsupervised, and semi-supervised ML algorithms and their implementation Build deep learning models for object detection, image classification, similarity learning, and moreBuild, deploy, and scale end-to-end deep neural network models in a production environmentBook DescriptionThis Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries.

You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more.

By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems

This Learning Path includes content from the following Packt

Mastering Machine Learning Algorithms by Giuseppe BonaccorsoMastering TensorFlow 1.x by Armando FandangoDeep Learning for Computer Vision by Rajalingappaa ShanmugamaniWhat you will learnExplore how an ML model can be trained, optimized, and evaluatedWork with Autoencoders and Generative Adversarial NetworksExplore the most important Reinforcement Learning techniquesBuild end-to-end deep learning (CNN, RNN, and Autoencoders) modelsWho this book is forThis Learning Path is for data scientists, machine learning engineers, artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model.

You will encounter the advanced intricacies and complex use cases of deep learning and AI. A basic knowledge of programming in Python and some understanding of machine learning concepts are required to get the best out of this Learning Path.

Table of ContentsMachine Learning Model FundamentalsIntroduction to Semi-Supervised LearningGraph-Based Semi-Supervised LearningBayesian Networks and Hidden Markov ModelsEM Algorithm and ApplicationsHebbian Learning and Self-Organizing MapsClustering AlgorithmsAdvanced Neural ModelsClassical Machine Learning with TensorFlowNeural Networks and MLP with TensorFlow and KerasRNN with TensorFlow and KerasCNN with TensorFlow and KerasAutoencoder with TensorFlow and KerasTensorFlow Models in Production with TF ServingDeep Reinforcement Learning

766 pages, Kindle Edition

Published December 21, 2018

About the author

Experienced and goal-oriented senior executive leader with wide expertise in the management of Artificial Intelligence, Machine Learning, Deep Learning, and Data Science projects for healthcare, B2C and Military industries (Fortune 500 firms).

His main interests include Machine/Deep Learning, Reinforcement Learning, Advanced Analytics, Bio-inspired adaptive systems, Business Intelligence, Neuroscience, Neural Language Processing, Econometrics, Data Science Strategy and Organization.

Professional member of IEEE, IEEE Computer Society, AAAI, ACM, IAENG, AICA, SFIA, and Agile Manifesto.